Modular Engineering Internship

Role Overview

As a Modular Engineering Intern, you will contribute to next-generation AI infrastructure and software technologies spanning the AI Inference stack. Depending on your background, interests, and project needs, you may work on:

• AI/ML model bring up
• AI Inference performance and optimization
• Compiler development
• The Mojo programming language
• GPU kernels and accelerator optimization
• Cloud and distributed systems
• Security
• Quality engineering
• Software development tooling

You may help optimize AI models for inference workloads, develop high-performance software for GPUs and emerging accelerators, improve compiler and runtime technologies for MAX or Mojo, scale distributed AI systems, develop AI infrastructure and model-serving platforms, build automated performance and quality frameworks, or improve developer tooling and CI/CD systems. This internship supports the Modular Engineering track.

Projects May Include

• AI Inference & Model Serving
• AI Systems & Infrastructure
• GPU & Kernel Engineering
• Compilers, Runtimes & Programming Languages
• Distributed & Cloud Systems
• AI Performance & Optimization
• AI Quality, Evaluation & Reliability
• Platform & Developer Engineering
• Security Engineering
• Software Development & Full-Stack Engineering
• Developer Tools & Open-Source Technologies

Minimum Qualifications

• Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer science, computer engineering, electrical engineering, software engineering, mathematics, artificial intelligence, or a related technical field.
• Must be available for 11–14 weeks during Summer 2027 (May–September).
• Expected graduation date of November 2027 or later.
• 1+ years of academic or project experience with programming languages such as Python, C, C++, Rust, Go, or related languages.

Preferred Qualifications

• Candidates actively pursuing a degree with an anticipated graduation between November 2027 and June 2028.
• Experience with machine learning, deep learning, generative AI, LLMs, transformer architectures, AI inference, or ML frameworks and ecosystems such as PyTorch, TensorFlow, or Hugging Face.
• Knowledge or experience with compiler design, compiler/runtime implementation, programming languages, library development, APIs, or frameworks such as MLIR, LLVM, GCC, TVM, or XLA.
• Experience with parallel computing, GPU programming, high-performance computing, CUDA, ROCm, or other accelerator programming models, including kernel optimization, computer architecture, memory systems, memory layouts, or data movement.
• Experience developing or optimizing AI/ML models and workloads, including model acceleration, quantization, profiling, benchmarking, inference optimization, performance analysis, or translating AI research and existing model implementations into production-ready code.
• Knowledge or experience with distributed systems, cloud infrastructure, large-scale AI infrastructure, model serving, Kubernetes, cloud-native technologies, multi-node or multi-cluster deployments, or scalable inference systems.
• Experience with systems programming or software development using Python, C/C++, Rust, Go, object-oriented languages, Python multiprocessing/asyncio, or related technologies.
• Experience with software engineering, application development, operating systems, APIs, software libraries, developer tooling, build systems, release automation, testing, CI/CD, or developer productivity tools.
• Familiarity with AI/ML evaluation, software quality, unit/integration testing, regression detection, performance testing, benchmarking, observability, reliability engineering, reproducibility, or failure analysis.
• Experience with profiling and performance-analysis tools such as NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, Linux perf, Intel VTune, or similar technologies.
• Knowledge or interest in software security, AI security, secure development, data protection, access controls, developer security tooling, AI-assisted coding tools, or secure AI systems and workflows.
• Experience building web, full-stack, AI/ML, or systems applications, developer integrations, technical examples, or other developer-facing solutions.
• Experience contributing to open-source projects, GitHub communities, technical documentation/blogs, developer education, or developer-focused technical content.
• Familiarity with AI developer and agentic coding tools or related developer workflows.
• Strong debugging, analytical, problem-solving, communication, and collaboration skills, with demonstrated curiosity and the ability to learn and operate effectively in a fast-paced engineering environment.
• Research experience or publications in relevant AI/ML, systems, compiler, or engineering areas is a plus.

Internship Program Highlights

• World-class teams to help define and build what is next for mobility and the world.
• Paired with a mentor with visibility to your project and team.
• Support from your manager and access to the entire Qualcomm employee community.
• Professional development workshops.
• Social events.
• Cross-functional speaker sessions.
• Executive speaker series.
• Competitive hourly pay.
• Accrued vacation time.
• Relocation coverage.
• Furnished housing accommodations for the duration of the internship.

Locations

Internship opportunities may be available in the following locations, depending on team and project needs:

• Austin
• Boulder
• Boxborough
• Bridgewater
• Chandler
• Irvine
• New York City
• Louisville
• Raleigh
• San Diego
• Santa Clara
• Seattle

Pay Range

$17.00 - $98.00